Summaries by Inventor Jha, Abhinav Kumar
Jha, Abhinav Kumar ; Liu, Ziping ; Moon, Hae Sol ; Rahman, Md Ashequr ; Yu, Zitong
T-019331
— Engineers in Washington University’s Computational Medical Imaging Lab have developed automated, machine-learning techniques to improve nuclear medicine imaging (SPECT and PET). These tools include estimation-based segmentation methods to define boundaries and ASC (attenuation and scatter comp…
Myocardial perfusion SPECT optimizationJha, Abhinav Kumar ; Rahman, Md Ashequr ; Siegel, Barry ; Yu, Zitong
T-020357
— T-020357, T-020358, T-020744 Myocardial perfusion SPECT optimization Technology Description Researchers from the laboratory of Abhinav Jha at Washington University have devised methods to reliably improve and personalize myocardial perfusion SPECT imaging. The inventions include the following capa…
Myocardial perfusion SPECT optimizationJha, Abhinav Kumar ; Rahman, Md Ashequr ; Siegel, Barry ; Yu, Zitong
T-020357
— T-020357, T-020358, T-020744 Myocardial perfusion SPECT optimization Technology Description Researchers from the laboratory of Abhinav Jha at Washington University have devised methods to reliably improve and personalize myocardial perfusion SPECT imaging. The inventions include the following capa…
Multi-energy window approaches for quantitative SPECTJha, Abhinav Kumar ; Li, Zekun ; Thorek, Daniel
T-019824
— T-019824 Multi-energy window approaches for quantitative SPECT Technology Description Researchers from the laboratory of Abhinav Jha at Washington University have devised a method to reliably obtain and significantly improve quantification of alpha-particle based SPECT tracer uptake, particularly …
Automated image processing tools for quantitative SPECT and PET scansJha, Abhinav Kumar ; Liu, Ziping ; Moon, Hae Sol ; Rahman, Md Ashequr ; Yu, Zitong
T-019331
— Engineers in Washington University’s Computational Medical Imaging Lab have developed automated, machine-learning techniques to improve nuclear medicine imaging (SPECT and PET). These tools include estimation-based segmentation methods to define boundaries and ASC (attenuation and scatter comp…
MRI neural network segmentation in atherosclerosisJha, Abhinav Kumar ; Li, Ran ; Woodard, Pamela ; Zheng, Jie
T-020254
— Technology Description Researchers at Washington University in St. Louis have developed a two-stage neural network model, with CNN and BNN architecture, to segment carotid atherosclerotic plaque components based on multi-weighted MR images and measure the uncertainty of the segmentation output. Th…
